what is decision analysis in operations research

What is Decision Analysis (DA)?

How decision analysis works, decision trees, expected value (ev), real-world example, additional resources, decision analysis (da).

A form of decision-making that involves identifying and assessing all aspects of a decision, and taking actions based on the decision that produces the most favorable outcome

Decision analysis (DA) is a form of decision-making that involves identifying and assessing all aspects of a decision, and taking actions based on the decision that produces the most favorable outcome.

The goal of decision analysis is to ensure that decisions are made with all the relevant information and options available. For example, a corporation may use it to make million-dollar investment decisions, or an individual can use it to decide on their retirement savings.

As a form of decision-making, the fundamentals of decision analysis can be used to solve a multitude of problems, from complex business issues to simple everyday problems.

• Decision analysis involves identifying and assessing all aspects of a decision, and taking actions based on the decision that produces the most favorable outcome.
• In decision analysis, models are used to evaluate the favorability of various outcomes.
• Decision trees are models that represent the probability of various outcomes in comparison to alternatives.

Decision analysis allows corporations to evaluate and model the potential outcomes of various decisions to determine the correct course of action. To be effective, the business needs to understand multiple aspects of a problem to result in a well-informed decision.

The analysis entails understanding various goals, outcomes, and uncertainties involved, including the use of probabilities to measure the expected outcome of various decisions.

One of the most important aspects involves framing the problem in a way that allows for further analysis. Framing is typically the first part of decision analysis, and it involves creating a framework to evaluate the problem from multiple perspectives. They can include opportunity statements, action items, and measures of success .

Once the framework is established, a model can be developed to evaluate the favorability of various outcomes. Examples of models are decision trees and influence diagrams.

After creating a framework to evaluate a problem, models are typically used to evaluate the outcomes of various decisions. Models are visual representations of expected outcomes, and they are used to illustrate decisions in comparison to other alternatives.

By modeling the various expected outcomes and their probabilities, businesses can then select the decision that produces a favorable outcome.

One of the most common models involved in decision analysis is decision trees, which are tree-shaped models with “branches” that represent potential outcomes.

Decision trees are used because they are simple to understand and provide valuable insight into a problem by providing the outcomes, alternatives, and probabilities of various decisions. This makes it easy to evaluate which decision results in the most favorable outcome.

After a model is constructed, it is important to find the expected value (EV) to evaluate which decision results in the most favorable outcome.

Recall that the decision trees provide all the possible outcomes in comparison to the alternatives. By calculating the expected value, we can observe the average outcomes of all decisions and then make an informed decision.

To calculate the expected value, we require the probability of each outcome and the resulting value. The formula for the expected value is as follows:

EV = (Probability A * Expected Payoff A) + (Probability B * Expected Payoff A)

The formula above assumes that a business decision has two outcomes – success or failure. Each outcome can be represented by Probability A or B. The Expected Payoff refers to the gain or loss expected with each outcome.

If there are multiple decisions to be made, a business will calculate the expected value for each decision to determine which is most favorable.

Let’s assume that a clothing store is opening a second location and wants to decide whether to open in San Francisco or New York. Opening a location in either city will involve different capital expenditures and demonstrate different rates of success.

Before constructing a decision tree, we will need to gather relevant data:

After gathering data, we can construct the decision tree based on each decision:

For each decision, the decision tree also includes numerical data to calculate the expected value. Squares represent decisions, and circles represent outcomes. The lines branching from squares are possible choices, while the lines branching from circles are expected outcomes.

The model also includes the costs associated with opening each location. To open in San Francisco, the store will need to invest $2 million, while a New York location will require an investment of $5 million.

The expected payoff amounts represent the potential revenue if the store succeeds, or the potential loss if the store fails.

To evaluate which decision is more favorable, we will calculate the expected value for each decision.

• EV (San Francisco) = (0.4 * $15,000,000) + (0.6 * -$4,000,000) = $3,600,000
• EV (New York) = (0.3 * $30,000,000) + (0.7 * -$10,000,000) = $2,000,000

Then, we must deduct the initial capital expenditure to find the net gain/loss:

• San Francisco : $3,600,000 – $2,000,000 = $1,600,000
• New York : $2,000,000 – $5,000,000 = -$3,000,000

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Decision Analysis (DA): Definition, Uses, and Examples

Katrina Ávila Munichiello is an experienced editor, writer, fact-checker, and proofreader with more than fourteen years of experience working with print and online publications.

What Is Decision Analysis (DA)?

Decision analysis (DA) is a systematic, quantitative, and visual approach to addressing and evaluating the important choices that businesses sometimes face. Ronald A. Howard, a professor of Management Science and Engineering at Stanford University, is credited with originating the term in 1964. The idea is used by large and small corporations alike when making various types of decisions, including management, operations, marketing, capital investments, or strategic choices.

Understanding Decision Analysis (DA)

Decision analysis uses a variety of tools to evaluate all relevant information to aid in the decision-making process and incorporates aspects of psychology, management techniques, training, and economics. It is often used to assess decisions that are made in the context of multiple variables and that have many possible outcomes or objectives. The process can be used by individuals or groups attempting to make a decision related to risk management , capital investments, and strategic business decisions.

Key Takeaways

• Decision analysis is a systematic, quantitative, and visual approach to making strategic business decisions.
• Decision analysis uses a variety of tools and also incorporates aspects of psychology, management techniques, and economics.
• Risk, capital investments, and strategic business decisions are areas where decision analysis can be applied.
• Decision trees and influence diagrams are visual representations that help in the analysis process.
• Critics argue that decision analysis can easily lead to analysis paralysis and, due to information overload, the inability to make any decisions at all.

A graphical representation of alternatives and possible solutions, as well as challenges and uncertainties, can be created on a decision tree or influence diagram. More sophisticated computer models have also been developed to aid in the decision-analysis process.

The goal behind such tools is to provide decision-makers with alternatives when attempting to achieve objectives for the business, while also outlining uncertainties involved and providing measures of how well objectives will be reached if final outcomes are achieved. Uncertainties are typically expressed as probabilities, while frictions between conflicting objectives are viewed in terms of trade-offs and utility functions . That is, objectives are viewed in terms of how much they are worth or, if achieved, their expected value to the organization.

Despite the helpful nature of decision analysis, critics suggest that a major drawback to the approach is " analysis paralysis ," which is the overthinking of a situation to the point that no decision can be made. In addition, some researchers who study the methodologies used by decision-makers argue that this type of analysis is not often utilized.

Examples of Decision Analysis

If a real estate development company is deciding on whether or not to build a new shopping center in a location, they might examine several pieces of input to aid in their decision-making process. These might include traffic at the proposed location on various days of the week at different times, the popularity of similar shopping centers in the area, financial demographics , local competition, and preferred shopping habits of the area population. All of these items can be put into a decision-analysis program and different simulations are run that help the company make a decision about the shopping center.

As another example, a company has a patent for a new product that is expected to see rapid sales for two years before becoming obsolete. The company is confronted with a choice of whether to sell the patent now or build the product in-house. Each option has opportunities, risks, and trade-offs, which can be analyzed with a decision tree that considers the benefits of selling the patent verses making the product in-house. Within those two branches of the tree, another group of decision trees can be created to consider such things as the optimal selling price for the patent or the costs and benefits of producing the product in-house.

Stanford University. " Stanford Profiles: Ronald Howard ." Accessed June 23, 2021.

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operations research (OR)

• Sarah Lewis

Operations research (OR) is an analytical method of problem-solving and decision-making that is useful in the management of organizations. In operations research, problems are broken down into basic components and then solved in defined steps by mathematical analysis.

The process of operations research can be broadly broken down into the following steps:

• Identifying a problem that needs to be solved.
• Constructing a model around the problem that resembles the real world and variables.
• Using the model to derive solutions to the problem.
• Testing each solution on the model and analyzing its success.
• Implementing the solution to the actual problem.

Disciplines that are similar to, or overlap with, operations research include statistical analysis , management science, game theory, optimization theory, artificial intelligence and network analysis. All of these techniques have the goal of solving complex problems and improving quantitative decisions.

The concept of operations research arose during World War II by military planners. After the war, the techniques used in their operations research were applied to addressing problems in business, the government and society.

Characteristics of operations research

There are three primary characteristics of all operations research efforts:

• Optimization- The purpose of operations research is to achieve the best performance under the given circumstances. Optimization also involves comparing and narrowing down potential options.
• Simulation- This involves building models or replications in order to try out and test solutions before applying them.
• Probability and statistics- This includes using mathematical algorithms and data to uncover helpful insights and risks, make reliable predictions and test possible solutions.

Importance of operations research

The field of operations research provides a more powerful approach to decision making than ordinary software and data analytics tools. Employing operations research professionals can help companies achieve more complete datasets, consider all available options, predict all possible outcomes and estimate risk. Additionally, operations research can be tailored to specific business processes or use cases to determine which techniques are most appropriate to solve the problem.

Uses of operations research

Operations research can be applied to a variety of use cases, including:

• Scheduling and time management.
• Urban and agricultural planning.
• Enterprise resource planning ( ERP ) and supply chain management ( SCM ).
• Inventory management .
• Network optimization and engineering.
• Packet routing optimization.
• Risk management .

Continue Reading About operations research (OR)

• The big picture of Operations Research

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Introduction to Operations Research

• First Online: 05 May 2023

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Operations research is a multi-disciplinary field that is concerned with the application of mathematical and analytic techniques to assist in decision-making. It employs techniques such as mathematical modelling, statistical analysis, and mathematical optimization as part of its goal to achieve optimal (or near optimal) solutions to complex decision-making problems.

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Importance of Operations Research in Decision-Making

Business managers face an endless list of complex issues every day. They must make decisions about financing, where to build a plant, how much of a product to manufacture, how many people to hire, and so on. Often, the factors that make up business issues are complicated, and they may be difficult to comprehend. Operations research is a way to deal with these thorny problems.

What Is Operations Research?

Operations research is a quantitative approach that solves problems, using a number of mathematical techniques. It is helpful to use operations research when you're trying to make decisions but the conditions are uncertain, and when differing objectives are in conflict with each other.

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Advantages of Operations Research

These mathematical techniques used in operations research help managers do their jobs more effectively:

Maintaining Better Control

Managers use techniques of operations research to maintain better control over their subordinates. This is possible because operations research provides a basis in which to establish standards of performance and ways to measure productivity. Reporting deviations from standards enables managers to identify problem areas and to take corrective action.

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Tools for gap analysis, flexible budgets & overhead analysis solutions in managerial accounting, process analysis vs. data analysis, how to solve linear programming in spreadsheet, adding to the thesaurus on microsoft word, better decision making.

The mathematical models of operations research allow people to analyze a greater number of alternatives and constraints than would usually be possible, if they were to use only an intuitive approach. Using operations research, it is easier to analyze multiple alternatives, which results in greater confidence in the optimal choice.

Better Coordination of Departments

Operations research analysis blends together the objectives of different departments. For example, operations research coordinates the aims of the marketing department with the schedules of the production department.

Increased Business Productivity

The mathematical formulas used in operations research can increase productivity, as they offer a greater number of optimal choices of inventory mix, plant machine utilization, factory size, manpower planning and implementing new technologies.

Operations Research Model

Operations research has evolved into a standard framework that's used for identifying and solving problems. The steps are as follows:

• Orientation
• Defining the problem
• Collecting data
• Formulating constraints and objectives
• Validating the model and output analysis
• Implementation and monitoring

Example of Operations Research Analysis

A good example of how to use operations research analysis is to consider the plight of farmer Jones. He must decide how many acres of corn and wheat to plant this year. One acre of corn will yield 10 bushels, and will require four hours of labor per week, and it will sell at $3 per bushel. Wheat will sell at $4 a bushel, will need 10 hours of labor a week and will yield 25 bushels per acre.

Farmer Jones has seven acres of land and can only work 40 hours per week. The government states that he must produce at least 30 bushels of corn in the coming year.

How many acres of corn and wheat does Farmer Jones plant to maximize his revenue? The linear programming technique of operations research gives the optimal answer – he should plant three acres of corn and 2.8 acres of wheat.

Operations Research Limitation: Problem must be Quantifiable

Operations research only functions when all factors in a problem can be quantified. Other relevant inputs to a problem might not be expressible in numbers.

Difficulties in Implementation

Implementing optimal solutions that result from operations research does not take human reactions and behavior into consideration.

Management is constantly under pressure to make economical decisions that result in more efficient operations and greater profits. The techniques of operations research help managers allocate resources more effectively and enables them to better optimize the performance of their businesses.

• University of Waterloo: Examples of Operations Research
• Journal of Multidisciplinary Engineering Science and Technology: Operational Research: A Study of Decision Making Process
• Columbia University: Introduction to Operations Research
• Institute for Operations Research and the Management Sciences

James Woodruff has been a management consultant to more than 1,000 small businesses. As a senior management consultant and owner, he used his technical expertise to conduct an analysis of a company's operational, financial and business management issues. James has been writing business and finance related topics for work.chron, bizfluent.com, smallbusiness.chron.com and e-commerce websites since 2007. He graduated from Georgia Tech with a Bachelor of Mechanical Engineering and received an MBA from Columbia University.

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Basic aspects

• Systems orientation
• The interdisciplinary team
• Methodology
• Problem formulation
• Model construction
• Deriving solutions from models
• Testing the model and the solution
• Implementing and controlling the solution
• Decision analysis and support
• New software tools for decision making
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• Linear programming
• Inventory control
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operations research , application of scientific methods to the management and administration of organized military, governmental, commercial, and industrial processes.

Operations research attempts to provide those who manage organized systems with an objective and quantitative basis for decision; it is normally carried out by teams of scientists and engineers drawn from a variety of disciplines . Thus, operations research is not a science itself but rather the application of science to the solution of managerial and administrative problems, and it focuses on the performance of organized systems taken as a whole rather than on their parts taken separately. Usually concerned with systems in which human behaviour plays an important part, operations research differs in this respect from systems engineering , which, using a similar approach, tends to concentrate on systems in which human behaviour is not important. Operations research was originally concerned with improving the operations of existing systems rather than developing new ones; the converse was true of systems engineering. This difference, however, has been disappearing as both fields have matured.

The subject matter of operations research consists of decisions that control the operations of systems. Hence, it is concerned with how managerial decisions are and should be made , how to acquire and process data and information required to make decisions effectively, how to monitor decisions once they are implemented , and how to organize the decision-making and decision-implementation process. Extensive use is made of older disciplines such as logic, mathematics, and statistics, as well as more recent scientific developments such as communications theory, decision theory , cybernetics, organization theory , the behavioral sciences, and general systems theory .

In the 19th century the Industrial Revolution involved mechanization or replacement of human by machine as a source of physical work. Study and improvement of such work formed the basis of the field of industrial engineering . Many contemporary issues are concerned with automation or mechanization of mental work. The primary technologies involved are mechanization of symbol generation (observation by machines such as radar and sonar), mechanization of symbol transmission (communication by telephone, radio, and television), and mechanization of logical manipulation of symbols (data processing and decision making by computer). Operations research applies the scientific method to the study of mental work and provides the knowledge and understanding required to make effective use of personnel and machines to carry it out.

What is Operations Research? | NC State OR

What is Operations Research?

Last Updated:  07/08/2024 | All information is accurate and still up-to-date

The Simple Answer: Operations Research (OR) is a discipline of problem-solving and decision-making. It uses advanced analytical methods to help management run an effective organization. Problems are broken down, analyzed and solved in steps.

• Identify a problem
• Build a model around the real-world problem
• Use the model and data to arrive at solutions
• Test the solution and analyze its success
• Implement the solution

The Technical Answer: Operations Research, also known as management sciences, uses scientific methods to study systems that require human decision-making. Consequently, OR helps make the most effective systems design and operation decisions. Moreover, OR’s strength and versatility come from its diagnostic power through observation and modeling and its prescriptive power through analysis and synthesis.

Additionally, OR is interdisciplinary, drawing on and contributing to techniques from many fields. These include mathematics, engineering, economics and physical sciences. Furthermore, OR practitioners have solved various real-world problems. These range from optimizing telecommunications networks to planning armed forces deployment during wartime. Many new applications, therefore, come from current energy production and distribution issues.

The CEO of the Future is an Engineer

Studies show three times as many S&P 500 CEOs hold degrees in engineering rather than business administration. This trend includes operations research practitioners among the next generation of engineers and scientists. They are tomorrow’s business leaders.

Operations Research Offers Workplace Freedom

Operations research practitioners have offices but also work in the settings they aim to improve. For example, when collecting data, they may observe restaurant staff or watch factory workers assembling parts. Additionally, when solving problems, they analyze data in an office. This combination of fieldwork and analysis creates a dynamic and flexible work environment.

The World Needs more Operations Research

As companies compete globally, the need for operations research practitioners grows. They are engineers trained to improve productivity and quality. Their common goal is saving companies money and increasing performance.

Operations Research is all about Options

Operations research practitioners work in almost any industry worldwide. They can work in and out of the office while interacting with people and processes they aim to improve. This flexibility gives them a career advantage over other types of engineers. Operations research practitioners don’t need to specialize, keeping their options open. Consequently, they remain immune to the ups and downs of any individual industry.

Careers in Operations Research

When considering a career in operations research, it’s logical to ask,  Will I be able to get a job?” Answer:  “YES”

Operation Research Continues to Grow

According to the Bureau of Labor , operations research jobs will grow over 32% between the years 2022-2032. This is faster than average for all occupations.

Companies Seek Efficiency

Every day, companies seek new ways to reduce costs and raise productivity. They rely on operations research practitioners to develop efficient processes and reduce costs, delays, and waste. This need drives job growth for these engineers, even in slow-growing or declining manufacturing industries.

Path to Management

Many operations research practitioners become managers because their work involves management tasks.

A Promising Future

It’s a great time to be an operations research practitioner. They solve problems, and there’s never a shortage of those!

Data Science and Decision Making: An Elementary Introduction to Modeling and Optimization

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What is Operations Research?

What is operations research #.

Good question. It is hard to say what exactly is operations research (often referred to simply as OR), in contrast to the the emerging areas of data science, or analytics, both of which have been gaining a lot of attention in recent years. For operations research, one such succinct statement might be:

a quantitative approach to decision-making in which a mathematical model of the problem setting is analyzed so as to provide precise guidance for attaining a desired objective.

However, this definition suffers from an attribute that is not unusual — this definition is based on still other terms that themselves would require a definition. For example, what is a “mathematical model”? What makes an approach “quantitative”? What is meant by “desired objective”? How does a mathematical model get “analyzed”?

Furthermore, there is nothing in any of this explanation that provides some understanding of why operations research is a reasonable name for the discipline. One easy answer is: it is not a good name for the discipline. A somewhat more useful answer is: the roots of the field go back to helping guide “operational” decisions. But what is an “operational” decision?

In the summer of 2020, there were a number of operational decisions that needed to be completely rethought because of the COVID-19 pandemic, starting with, is it “safe” to reopen the campus for in-person education? That led to a million-and-one follow-up questions, all seeking to provide guidance to the more general question, how might we most safely “run” the university if we elect to have in-person education? Since this is a very general question, one might ask a more precisely focused one — if we have target limits on the number of COVID-19 cases that might occur at Cornell throughout the semester, and we can test each student every \(k\) days, what is the smallest value of \(k\) that provides reasonable assurance of not exceeding those targets? Here we again need to ask, what is “reasonable assurance”? One element of building a model for this sort of question relies on the mathematical framework of probability, which captures notions of “likelihood”. And partnering with such a probabilistic framework are statistical tools: for example, one might model one mode of transmission by the statement — if two people are within 6’ of each other for one hour, there is an \(\alpha\) percent chance of a person who is positive for COVID-19, infecting the other — but this gives rise to the associated statistical problem, how can you use historical data to estimate \(\alpha\) ? Indeed, models to answer this type of question were used to guide the decisions that informed the university’s response to the pandemic and have been led by Cornell faculty and students in operations research.

Although probabilistic and statistical models are extremely important aspects of operations research, this course will focus on so-called deterministic optimization models (i.e., those without a probabilistic element in their set-up); probability and statistics are (mostly) left to be introduced in the subsequent gateway course in operations research, ENGRD,2700.

Let us start with a very simple example of an optimization problem that was, for those students attending this class in person in Fall 2020, literally staring them in the face. Each student attending in person was in an assigned seat that is at least 6’ from anyone else. (To be precise, the center of each occupied seat was at least 6’ from the outer edge of any other occupied seat.) The seats are in fixed positions. How do we select in which seats to assign students so that the maximum number of students possible can attend a given lecture? This is an operational question, since its answer is one critical element in the functioning of the university. This course will provide a precise language to state this optimization problem and provide algorithmic and computational tools to solve problems of this sort - in fact, one later lab exercise will be to attack this very application. OR optimization tools were also used heavily in redesigning the Cornell fall course roster, which needed to be completely reworked between the time that the decision was made to reopen with in-person classes in early July and August 26th when enrollment commenced.

Hopefully, the examples above have given you a rough idea about some of the kinds of questions that can be addressed with the tools developed by OR, but these are just a very limited set of examples. One of the exciting aspects of OR is that the applications of this discipline come from many, many different areas — health care, environmental preservation, computer design, transportation logistics, financial instruments, genetics, …, the list goes on and on.

A good next step might be to consider one specific example, and study it more in depth, to help understand the rather hard-to-interpret definition of OR given above.

A colleague who was studying for his PhD in astronomy posed the following question. For his dissertation, he was studying a particular set of stars. Every few months, he would be allocated one week of access to a powerful radio telescope. The telescope was programmable, and throughout the week, would focus on one particular star for a given length of time (roughly 10 minutes), acquiring data from the signal from that star, and then would be re-positioned to focus on the next star, and so forth. Since it was a radio telescope, this proceeded for a 24-hour cycle (e.g., it could receive the signal 24/7), when the process would begin anew. The time spent re-positioning was, from his perspective, wasted time. It is easy to imagine if the telescope proceeded through the stars in a completely random order, there might be quite a lot of time wasted in re-positioning. In words, the optimization problem (very roughly) is to maximize the amount of time left to observe the stars under investigation.

One natural question is: how were these decisions being made before? The answer here seemed pretty worrisome — there was a “master list” of all of the known stars in the sky, and this induced an ordering of the relatively few stars in the set that were being studied by this colleague. That determined which star would be observed next.

We want to build a mathematical model to guide this decision-making process. The first element of such a model is to understand what the input to such a model might be — what data do we need in order to provide advice for this colleague? One first idea might be to that the desired input is the positional location of the stars being studied. While this is clearly useful, it might not be the entire story. The goal here is to spend as little time as possible re-positioning the telescope; that is, we need to know how the time to re-position the telescope depends on the positions of the stars observed consecutively. So, for example, if we move from observing Alpha Centuri to next observing Beta Orionis, we need to know the time that would elapse between completing the first observation, and being ready to start the second, which might be denoted

To simplify matters, suppose that there are 100 stars that we wish to observe, and we will call them \(1,2,...,100\) . We denote the set of all 100 stars by \(\{1,2,...,100\}\) — this is the usual mathematical notation for denoting the set consisting of the integers \(1\) through \(100\) . We can use \(i\) and \(j\) as variables to denote two arbitrary stars in our set — this would be denoted \(i \in \{ 1,2,...,100\}\) and \(j \in \{1,2,...,100\}\) , where \(i\not = j\) . We would then need the data, \(\text{Focus-time[}i \text{, }j\text{]}\) , for each such pair of stars.

By this point, someone who has studied OR a bit (say, a senior majoring in it at Cornell), but has not seen this telescope observational problem might have the bright idea — “oh, this is just the traveling salesman problem!” And indeed, they would be, more or less, completely right. In words, the traveling salesman problem is often stated as follows: a peddler needs to visit each city exactly once in a given set of cities, starting and ending at home, so as to minimize the total time spent traveling. How should the peddler proceed? In our problem, we have stars, not cities; we have re-positioning times, not travel times, but what we have done is the first conceptual steps in modeling our new application as a traveling salesman problem, which is the first topic in this course.

What is Operations Research (OR)? Definition, Concept, Characteristics, Tools, Advantages, Limitations, Applications and Uses

• Post last modified: 20 July 2022
• Reading time: 25 mins read
• Post category: Operations Research

What is Operations Research (OR)?

Operations Research (OR) may be defined as the science that aims for the application of analytical and numerical techniques along with information technology to solve organisational problems. There are various definitions of OR in the literature.

Table of Content

• 1 What is Operations Research (OR)?
• 2 Operations Research Definition
• 3 Concept of Operations Research
• 4 History of Operations Research
• 5.1 OR as a decision-making approach
• 5.2 OR as a scientific approach
• 5.3 OR as a computer-based approach
• 6 Objectives of Operations Research
• 7.1 Linear Programming
• 7.2 Simulation
• 7.3 Statistics
• 7.4 Assignment
• 7.5 Queuing Theory
• 7.6 Game Theory
• 7.7 Non-linear Programming
• 7.8 Dynamic Programming
• 7.9 Goal Programming
• 7.10 Network Scheduling
• 8.1 Increased productivity
• 8.2 Optimised outcomes
• 8.3 Better coordination
• 8.4 Lower failure risk
• 8.5 Improved control on the system
• 9.1 High costs
• 9.2 Dependence on technology
• 9.3 Reliance on experts
• 9.4 Unquantifiable factors
• 10.1 Resource distribution in projects
• 10.2 Project scheduling, monitoring and control
• 10.3 Production and facilities planning
• 10.4 Marketing
• 10.5 Personnel management
• 10.6 Supply chain management

Operations Research Definition

Some of the well-known operations research definitions are as:

Moarse and Kimbal (1946) defined OR as a scientific method of providing the executive department a quantitative basis for decision-making regarding the operations under their control.

According to Churchman, Ackoff and Arnoff (1957), OR is the application of scientific methods, techniques and tools to operational problems so as to provide those in control of the system an optimum solution to the problem.

McGraw-Hill Science & Technology Encyclopaedia states that OR is the application of scientific methods and techniques to decision-making problems.

Britannica Concise Encyclopaedia defines OR as the application of scientific methods to the management and administration of military, government, commercial, and industrial processes.

Decision-making problems arise when there are two or more alternative courses of action, each resulting in a different outcome. The goal of OR is to help select the alternative that will maximise the use of available resources and lead to the best possible outcome.

In this article, we introduce the topic of operation research that will allow students to gain an insight into the basic concepts of operations research. This will give them a better understanding of the upcoming chapters.

Concept of Operations Research

Decision-making is not a simple task in today’s socio-economic environment. Complex problems such as transportation, queuing, etc., are routinely presented and dealt with at the operational level. Moreover, higher attention is now being paid to a wide range of tactical and strategic problems.

Decision makers cannot afford to take decisions by simply taking their personal experiences or intuitions into account. Decisions made in the absence of suitable information can have seri- ous consequences. Being able to apply quantitative methods to deci- sion-making is, therefore, vital to decision-makers.

OR is a field of applied mathematics that makes use of analytical tools and mathematical models to solve problems and aid the management in decision-making. OR is an approach that allows decision makers to compare all possible courses of action, understand the likely outcomes and test the sensitivity of the solution to modifications or errors.

OR helps in making informed decisions, allocating optimal resource and improving the performance of systems. According to Ackoff (1965), the development (rather than the history) of OR as a science consists of the development of its methods, concepts, and techniques.

OR is neither a method nor a technique; it is or is becoming a science and as such is defined by a combination of the phenomena it studies.

History of Operations Research

The beginning of OR as a formal discipline can be traced back to 1937 when A.P. Rowe, Superintendent of the Bawdsey Research Station in the British Royal Air Force, sought British scientists to assist military leaders in the use of the recently developed radar system to detect enemy aircraft.

A few years later, the British Army and the Royal Navy also incorporated OR, again for assistance with the radar system. All three of Britain’s military services had set up formal OR teams by 1942. Similar developments took place in other countries (of which the most significant are those of the United States, in terms of further development of the discipline).

Once World War II ended, several British operations researchers relocated to government and industry. By the 1950s, the United States government and industry also incorporated OR programmes. In India, it was in 1949 when an Operation Research unit was set up at Regional Research Laboratory, Hyderabad, that OR came into being.

An OR team was also established at Defense Science Laboratory to resolve inventory, purchase and planning issues. The 1950s saw continual growth in the application of OR methods to non-defence activities in India. In 1953, the Indian Statistical Institute, Calcutta established an OR unit for national planning and survey-related issues. OR also became useful in the Indian Railways to resolve ticketing issues, train scheduling problems and so on.

Since then, OR, as a formal discipline, has expanded continuously in the last 70 years and is widely recognised as a central approach to decision-making in the management of different domains of an organisation.

With accessibility to faster and flexible computing facilities, OR has expanded further and is widely used in industry, finance, logistics, transportation, public health and government. One needsCharacteristics of Operations Research to bear in mind that OR is still a fairly new scientific discipline, despite its rapid evolution. This means that its methodology, tools and techniques, and applications still continue to grow rapidly.

Characteristics of Operations Research

OR aims to find the best possible solution for any problem. Its main goal is to help managers obtain a quantitative basis for decision-making. This results in increased efficiency, more control and better coordination in the organisation when fulfilling the required objectives.

OR is an interdisciplinary field involving mathematics and science. OR uses statistics, algorithms and mathematical modelling to provide the best possible solutions for complex problems. OR basically involves optimising the maxima or minima functions.

For example, a business problem could be the maximisation of profit, performance or yield or it could be related to minimising risk and loss. OR has various characteristics based on the different objectives for which it is used.

The characteristics of operations research (OR) are explained as follows:

OR as a decision-making approach

All organisations are faced with situations where they need to select the best available alterna- tive to solve a problem. OR techniques help managers in obtaining optimal solutions for their problems.

Additionally, OR techniques are also used by managers to understand the problems at hand in a better manner and make effective decisions. It is important to note that OR techniques help in improving the quality of decisions.

OR helps in finding bad answers to problems having worse answers. It means that for many problems, OR may not be able to give perfect replies but can help in improving the quality of decisions.

OR as a scientific approach

OR uses multiple scientific models along with tools and techniques to resolve complex problems while eliminating individual biasness. The scientific method involves observing and defining a problem, formulating and testing the hypothesis and analysing the results of the test. The results of the test determine whether the hypothesis should be accepted or rejected.

OR as an interdisciplinary approach: Since OR focuses on complex organisational problems, it includes expertise from different disciplines such as mathematics, economics, science and engineering. Having different experts ensures that the problem is analysed from different perspectives and alternative strategies are evolved for the selected problem.

Some of the complex problems that can be solved using OR include deciding or choosing optimal dividend policies, investment portfolio management, auditing, balance sheet and cash flow analysis, selection of product mix, marketing and export planning, advertising, media planning and packaging, procurement and exploration, optimal buying decisions, transportation planning, facilities planning, location and site selection, production cost and methods, assembly line, blending, purchasing and inventory control, etc.

OR as a systems approach: In OR, important interactions and their influence on the organisation as a whole are considered for decision-making. OR looks at problems from the perspective of the organisation:

• To determine the potential for enhancing the performance of the system as a whole
• To measure the impact of alterations in variables on the whole system
• To find reasons for the malfunctioning of the system as a whole

OR as a computer-based approach

OR solves business problems using mathematical models, manipulating large amount of data and performing computations on these large data sets. It is almost impossible to do such computations and manipulations manually. Therefore, most OR-based problems are solved using computers.

Objectives of Operations Research

Operations research in an organisation is responsible for managing and operating as efficiently as possible within the given resources and constraints. In case of complex problems as listed in the previous section, normal analysis does not work and in such cases, OR approach helps an organisation in reaching a viable solution.

OR is basically a problem-solving and decision-making tool used by organisations for enhancing their productivity and performance. Apart from this, certain other objectives of OR are as follows:

• Solving operational questions
• Solving queries related to resources’ operations such as human resource scheduling, machine and material scheduling, utilisation of funds, etc.
• Making informed decisions
• Improving the current systems
• Predicting all possible alternative outcomes
• Evaluating risks associated with each alternative

OR is needed for the following reasons:

• If the problem is a recurring one, it may make sense to create a model to make decision-making faster and better. OR provides a readymade model or process in such cases to help create a suitable model.
• OR provides an analytical, logical and quantitative basis to represent the problem
• OR models help in making sound decisions and decreases the risk of flawed decisions

Tools of Operations Research

OR is widely used in industries, businesses, governments, military establishments and agriculture. Most importantly, OR techniques are used by organisations. All the business decision areas, such as planning production and facilities, scheduling projects, minimising procurement costs, and selecting a product mix, which require optimisation of an objective, fall under the domain of operation research. OR uses a variety of tools to solve different business problems.

The most commonly used tools of OR are discussed below:

• Linear Programming

Organisations use the Linear Programming (LP) technique to determine the optimal solutions that may be defined as either most profitable or least cost solutions. Businesses use LP techniques to assign jobs to machines, select product mix, select advertising media, select an investment portfolio, etc.

Simulation is another important OR tool wherein an expert con- structs a model that replicates a real business scenario. Simulation is extremely useful in cases where actual market testing is risky or impossible due to various reasons such as high expenditure.

It has widely been used in a variety of probabilistic marketing situations. For example, finding the Net Present Value (NPV) distribution of the market introduction of a product.

Statistics allows an organisation to evaluate the risks present in all the domains of the business. It enables an organisation to predict future trends and thus makes informed business decisions. The OR team compares different trade-offs and chooses the best alternative.

For example, statistics is used in solving various real-life problems such as deterministic optimisation. Some of the problems where statistics serve as the primary vehicle for OR include decision theory, optimal strategies for search engine marketing, credit scoring, queuing theory, stochastic programming and inventory management.

The assignment method deals with the issue of how to allocate a fixed number of facilities to different tasks in the most optimal manner. The aim is to minimise the cost/time of completing a number of tasks by a number of agents (person or equipment). For example, assigning method can be used to assign specific workers to specific tasks.

Queuing Theory

If a problem involves queuing, the Queuing or Waiting Line theory is used. Using this tool, the expected number of people waiting in line, expected waiting time, expected idle time for the server and so on can be calculated. Queuing theory can be used to solve problems related to traffic congestion, repair and maintenance of broken machines, air traffic scheduling and control, scheduling bank counters, etc

Game Theory

Game theory is useful in decision-making in cases where there are one or more opponents (or players) with conflicting interests. Just as in a game, where the success of one person is influenced by the choices made by the opponent, in the game theory, the actions of all the players influence the outcomes.

For example, game theory is used for selecting war strategies and military decisions, bidding at auctions, negotiations, product pricing, stock market decisions, etc.

Non-linear Programming

Non-linear problems are similar to linear problems except that they have at least one non-linear function or constraint. Non-linear models become useful in cases where the objective function of some of the constraints is not linear in nature.

For instance, a non-linear programming is used for making optimal decisions in the production process, optimising fractionated protocols in cancer radiotherapy, training recur- rent neural networks in time series prediction problems, etc.

Dynamic Programming

Dynamic programming models deal with problems in which decisions need to be made over multiple stages in a sequence and the current decisions affect both present and future stages.

For example, dynamic programming is used by Google Maps to find the shortest path between a source and a destination. It is also used in networking to sequentially transfer data from one sender to various receivers.

Goal Programming

Goal programming tools allow organisations to handle multiple and incompatible objectives. These models are quite similar to linear programming models with the difference being that goal programming can have multiple objectives whereas linear programs have only one.

For example, goal programming can be applied to corporate budgeting, financial planning, working capital management, financing decisions, commercial bank management, accounting control, etc.

Network Scheduling

Network scheduling methods are useful in planning, scheduling and monitoring projects of large scales common in construction industry, information technology, etc.

For example, network scheduling is used for assembly line scheduling, inventory planning and control, launching new advertisement campaigns, installing new equipment, controlling projects, etc.

Advantages of Operations Research

The field of OR contains robust tools that can be applied in a variety of fields such as transportation, warehouse, production management, assignment of jobs, etc. The application of OR tools and techniques helps in making the best decisions with the available data.

There are many advantages of OR, as shown in Figure:

Increased productivity

OR helps in increasing the productivity of organisations to a huge extent. The use of OR for effective control of operations allows the managers to take informed decisions. Effective and precise decision-making leads to improvement in the productivity of an organisation.

OR tools also help increase the efficiency of various routine tasks in an organisation such as inventory control, workforce-related, business expansion, technology upgrades, installation etc. All these ultimately contribute towards productivity improvement.

Optimised outcomes

Management is responsible for making various important decisions about the organisation. OR tools can be used by the management to find out various alternative solutions to a problem and selecting the best solution. Selection is based on the profits accrued and costs incurred.

Better coordination

OR can be used to synchronise the objectives of different departments which results in achieving the goals of all departments. Managers belonging to different departments become aware of the common objectives of the organisation, which ensures that different departments coordinate towards achievement of the said goals.

For example, OR helps in coordinating the goals of the marketing department with the production department schedule.

Lower failure risk

Using OR tools and techniques, managers can find all the alternative solutions and risks associated with a given problem. Prior information with respect to all the possible risks helps in reducing the risks of failure.

Improved control on the system

Managers can apply OR to take better control of the work since it provides comprehensive information about any given course of action. Since OR informs managers about the expected outcome, they can determine what standards of performance need to be expected from employees.

They can compare the actual performance of the employees with the standard performance and, therefore, control them in a better manner. It also enables managers to prioritise tasks in terms of their importance.

Limitations of Operations Research

There is no doubt with respect to the practical utility and usability of OR and its applications in real life. However, OR also suffers from several limitations as shown in Figure:

High cost is one of the biggest limitations of OR. It not only needs expensive technology to create mathematical equations but also experts to perform simulations. Therefore, while OR does provide effective solutions to a particular problem, it comes with a high cost attached.

Dependence on technology

OR is heavily reliant on technology. Computers are generally needed to model and analyse OR problems. Since technology is quite costly as well as subject to failure, its use is severely restricted.

Reliance on experts

OR requires a team of experts from different fields to perform the assessments. Hiring multiple experts can be costly. In addition, maintaining good communication and coordination among experts and making all experts work together is a critical task.

Unquantifiable factors

It is known that OR tools are based on mathematical models that include various information based on quantifiable factors. It means that the efficacy of a solution provided by OR tool depends on quantifiable factors.

However, there are certain important unquantifiable factors that cannot be included in the models. When this happens, solutions can often be inexact, inaccurate and therefore, inefficient.

Applications and Uses of Operations Research in Management

The list of OR applications is notable, given its considerable involvement in various managerial and decision-making processes at several organisational levels. It can be applied in a wide range of industries to help with complex problems in planning, policy-making, scheduling, forecasting, resource allocation, process analysis, etc.

It may be employed by virtually any industry to determine the best solution to any problem. Various human activities that need optimisation of resources can use OR.

The following are some areas where OR may be applied:

Resource distribution in projects

Various OR tools are used to determine which resources are to be allocated to which activities. For instance, OR can help in determining the allocation of ‘n’ number of jobs among two machines. Similarly, OR can also be applied to determine and allocate materials, workforce, time and budget to projects.

Project scheduling, monitoring and control

OR is applied to activities involving scheduling, inventory control, improvement of workflow, elimination of bottlenecks, business process re-engineering, capacity planning and general operational planning.

OR tools such as the Critical Path Method (CPM) and Project Evaluation and Review Technique (PERT) are used for scheduling the different activities involved in a project. In addition, these tools are also used for continuous monitoring and control of the project.

Production and facilities planning

OR can be applied for activities involving site selection, factory size, facility planning, inventory forecasts, calculation of economic order quantities, computing reorder levels, maintenance policies, replacement policies, manpower planning, and assembly line scheduling, etc. All the important decisions and planning work related to facilities, manufacturing and maintenance can be completed using OR tools.

Application of OR can be done in budget allocation for advertising, choice of advertising media and product launch timing. For instance, how should a company allocate its budget for advertising a newly launched product on two TV channels, TV1 and TV2 within a given budget. A company may also use OR techniques to find out how many units of each product in a product mix should be produced to maximise demand.

Personnel management

OR also finds application in manpower planning, scheduling of training programs, wage administration, etc.

Finance and accounting: The application of OR in finance is concerned with effective capital planning, cash flow analysis, capital budgeting, credit policies, investment analysis and decisions, establishing costs for by-products and developing standard costs, portfolio management, risk management, etc.

Supply chain management

The application of OR in Supply Chain Management involves decision-making regarding the transportation of goods for the purpose of manufacturing and distribution. This further involves the selection of the shortest optimal routes so that the goods can be transported to maximum locations at minimum costs.

Business Ethics

( Click on Topic to Read )

• What is Ethics?
• What is Business Ethics?
• Values, Norms, Beliefs and Standards in Business Ethics
• Indian Ethos in Management
• Ethical Issues in Marketing
• Ethical Issues in HRM
• Ethical Issues in IT
• Ethical Issues in Production and Operations Management
• Ethical Issues in Finance and Accounting
• What is Corporate Governance?
• What is Ownership Concentration?
• What is Ownership Composition?
• Types of Companies in India
• Internal Corporate Governance
• External Corporate Governance
• Corporate Governance in India
• What is Enterprise Risk Management (ERM)?
• What is Assessment of Risk?
• What is Risk Register?
• Risk Management Committee

Corporate social responsibility (CSR)

• Theories of CSR
• Arguments Against CSR
• Business Case for CSR
• Importance of CSR in India
• Drivers of Corporate Social Responsibility
• Developing a CSR Strategy
• Implement CSR Commitments
• CSR Marketplace
• CSR at Workplace
• Environmental CSR
• CSR with Communities and in Supply Chain
• Community Interventions
• CSR Monitoring
• CSR Reporting
• Voluntary Codes in CSR
• What is Corporate Ethics?

Lean Six Sigma

• What is Six Sigma?
• What is Lean Six Sigma?
• Value and Waste in Lean Six Sigma
• Six Sigma Team
• MAIC Six Sigma
• Six Sigma in Supply Chains
• What is Binomial, Poisson, Normal Distribution?
• What is Sigma Level?
• What is DMAIC in Six Sigma?
• What is DMADV in Six Sigma?
• Six Sigma Project Charter
• Project Decomposition in Six Sigma
• Critical to Quality (CTQ) Six Sigma
• Process Mapping Six Sigma
• Flowchart and SIPOC
• Gage Repeatability and Reproducibility
• Statistical Diagram
• Lean Techniques for Optimisation Flow
• Failure Modes and Effects Analysis (FMEA)
• What is Process Audits?
• Six Sigma Implementation at Ford
• IBM Uses Six Sigma to Drive Behaviour Change
• Research Methodology
• What is Research?
• What is Hypothesis?
• Sampling Method
• Research Methods
• Data Collection in Research
• Methods of Collecting Data
• Application of Business Research
• Levels of Measurement
• What is Sampling?
• Hypothesis Testing
• Research Report
• What is Management?
• Planning in Management
• Decision Making in Management
• What is Controlling?
• What is Coordination?
• What is Staffing?
• Organization Structure
• What is Departmentation?
• Span of Control
• What is Authority?
• Centralization vs Decentralization
• Organizing in Management
• Schools of Management Thought
• Classical Management Approach
• Is Management an Art or Science?
• Who is a Manager?

Operations Research

• What is Operations Research?
• Operation Research Models
• Linear Programming Graphic Solution
• Linear Programming Simplex Method
• Linear Programming Artificial Variable Technique

Duality in Linear Programming

• Transportation Problem Initial Basic Feasible Solution
• Transportation Problem Finding Optimal Solution
• Project Network Analysis with Critical Path Method

Project Network Analysis Methods

Project evaluation and review technique (pert), simulation in operation research, replacement models in operation research.

Operation Management

• What is Strategy?
• What is Operations Strategy?
• Operations Competitive Dimensions
• Operations Strategy Formulation Process
• What is Strategic Fit?
• Strategic Design Process
• Focused Operations Strategy
• Corporate Level Strategy
• Expansion Strategies
• Stability Strategies
• Retrenchment Strategies
• Competitive Advantage
• Strategic Choice and Strategic Alternatives
• What is Production Process?
• What is Process Technology?
• What is Process Improvement?
• Strategic Capacity Management
• Production and Logistics Strategy
• Taxonomy of Supply Chain Strategies
• Factors Considered in Supply Chain Planning
• Operational and Strategic Issues in Global Logistics
• Logistics Outsourcing Strategy
• What is Supply Chain Mapping?
• Supply Chain Process Restructuring
• Points of Differentiation
• Re-engineering Improvement in SCM
• What is Supply Chain Drivers?
• Supply Chain Operations Reference (SCOR) Model
• Customer Service and Cost Trade Off
• Internal and External Performance Measures
• Linking Supply Chain and Business Performance
• Netflix’s Niche Focused Strategy
• Disney and Pixar Merger
• Process Planning at Mcdonald’s

Service Operations Management

• What is Service?
• What is Service Operations Management?
• What is Service Design?
• Service Design Process
• Service Delivery
• What is Service Quality?
• Gap Model of Service Quality
• Juran Trilogy
• Service Performance Measurement
• Service Decoupling
• IT Service Operation
• Service Operations Management in Different Sector

Procurement Management

• What is Procurement Management?
• Procurement Negotiation
• Types of Requisition
• RFX in Procurement
• What is Purchasing Cycle?
• Vendor Managed Inventory
• Internal Conflict During Purchasing Operation
• Spend Analysis in Procurement
• Sourcing in Procurement
• Supplier Evaluation and Selection in Procurement
• Blacklisting of Suppliers in Procurement
• Total Cost of Ownership in Procurement
• Incoterms in Procurement
• Documents Used in International Procurement
• Transportation and Logistics Strategy
• What is Capital Equipment?
• Procurement Process of Capital Equipment
• Acquisition of Technology in Procurement
• What is E-Procurement?
• E-marketplace and Online Catalogues
• Fixed Price and Cost Reimbursement Contracts
• Contract Cancellation in Procurement
• Ethics in Procurement
• Legal Aspects of Procurement
• Global Sourcing in Procurement
• Intermediaries and Countertrade in Procurement

Strategic Management

• What is Strategic Management?
• What is Value Chain Analysis?
• Mission Statement
• Business Level Strategy
• What is SWOT Analysis?
• What is Competitive Advantage?
• What is Vision?
• What is Ansoff Matrix?
• Prahalad and Gary Hammel
• Strategic Management In Global Environment
• Competitor Analysis Framework
• Competitive Rivalry Analysis
• Competitive Dynamics
• What is Competitive Rivalry?
• Five Competitive Forces That Shape Strategy
• What is PESTLE Analysis?
• Fragmentation and Consolidation Of Industries
• What is Technology Life Cycle?
• What is Diversification Strategy?
• What is Corporate Restructuring Strategy?
• Resources and Capabilities of Organization
• Role of Leaders In Functional-Level Strategic Management
• Functional Structure In Functional Level Strategy Formulation
• Information And Control System
• What is Strategy Gap Analysis?
• Issues In Strategy Implementation
• Matrix Organizational Structure
• What is Strategic Management Process?

Supply Chain

• What is Supply Chain Management?
• Supply Chain Planning and Measuring Strategy Performance
• What is Warehousing?
• What is Packaging?
• What is Inventory Management?
• What is Material Handling?
• What is Order Picking?
• Receiving and Dispatch, Processes
• What is Warehouse Design?
• What is Warehousing Costs?

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What does an operations research analyst do?

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What is an Operations Research Analyst?

An operations research analyst applies advanced analytical and mathematical techniques to solve complex problems and optimize decision-making in various industries. These analysts use mathematical modeling, statistical analysis, and computer simulations to analyze and improve organizational processes, systems, and resource allocation. They work with large sets of data and develop mathematical models and algorithms to assist in decision-making, improve efficiency, and maximize outcomes.

Operations research analysts work on a wide range of problems, including supply chain optimization, production planning, scheduling, inventory management, logistics, and facility layout. They use their expertise to formulate and solve mathematical models that represent real-world scenarios, considering factors such as constraints, uncertainties, and objectives. By analyzing data and running simulations, they can evaluate different scenarios and recommend the best course of action to optimize performance, reduce costs, increase productivity, and improve overall operational efficiency.

What does an Operations Research Analyst do?

Operations research applies quantitative methods and analytical techniques to improve processes, systems, and resource allocation in various industries.

Duties and Responsibilities The duties and responsibilities of an operations research analyst can vary depending on the specific industry, organization, and project requirements. However, here are some common responsibilities associated with this role:

• Problem Identification and Formulation: Operations research analysts work closely with stakeholders to understand the objectives and challenges of a given problem or decision-making process. They identify the key variables, constraints, and objectives and translate them into a mathematical or analytical model.
• Data Collection and Analysis: Analysts gather relevant data from various sources, including databases, surveys, and other sources. They clean and preprocess the data, perform statistical analysis, and apply mathematical modeling techniques to derive insights and patterns.
• Mathematical Modeling and Optimization: Operations research analysts develop mathematical models, algorithms, and optimization techniques to represent the problem at hand. They use tools such as linear programming, integer programming, simulation, and other techniques to analyze the model and identify optimal solutions or decision-making strategies.
• Simulation and Scenario Analysis: Analysts utilize simulation tools and techniques to model complex systems and evaluate different scenarios. They run simulations to assess the impact of various decisions, policies, or system changes on performance metrics and outcomes.
• Decision Support and Recommendations: Based on the analysis and optimization results, operations research analysts provide decision support to stakeholders. They interpret the findings, present recommendations, and communicate the implications of different options to assist in informed decision-making.
• Implementation and Monitoring: Analysts collaborate with relevant teams to implement recommended solutions or changes. They may assist in the deployment of new systems, processes, or strategies and monitor their effectiveness to ensure that the desired outcomes are achieved.
• Continuous Improvement and Research: Operations research analysts stay updated with advancements in the field, continuously explore new techniques and methodologies, and contribute to research and development efforts. They seek opportunities for process improvement and provide ongoing support to optimize operations and decision-making.
• Collaboration and Communication: Analysts work collaboratively with cross-functional teams, stakeholders, and subject matter experts. They communicate complex analytical concepts and findings in a clear and concise manner, both verbally and through reports or presentations.

Fields of Work While operations research analysts can be employed in a wide range of industries, their expertise is particularly valuable in sectors that involve complex operational and logistical challenges. Some common fields where operations research analysts are employed include:

• Transportation and Logistics: Operations research analysts play a vital role in optimizing transportation networks, improving route planning, scheduling, and resource allocation for shipping, distribution, and supply chain management.
• Manufacturing and Production: Operations research analysts work on optimizing production planning, inventory management, scheduling, and facility layout to enhance efficiency, reduce costs, and improve productivity in manufacturing and production processes.
• Healthcare: In the healthcare industry, operations research analysts analyze patient flow, resource allocation, hospital scheduling, healthcare delivery optimization, and healthcare resource planning to improve operational efficiency and patient outcomes.
• Finance and Risk Management: Operations research analysts apply mathematical models and optimization techniques to analyze financial markets, portfolio management, risk assessment, and risk management to help financial institutions make informed decisions and mitigate risks.
• Energy and Utilities: Operations research analysts contribute to optimizing energy production and distribution systems, grid management, resource allocation, and demand forecasting to improve energy efficiency and ensure reliable supply.
• Defense and Homeland Security: Operations research analysts work on strategic planning, resource allocation, logistics, and decision support systems to optimize military operations, defense planning, and homeland security initiatives.
• Consulting and Analytics: Many operations research analysts work in consulting firms or analytics companies, where they provide expertise in optimization, decision support, and data analysis to clients across multiple industries.

Types of Operations Research Analysts Operations research analysts can specialize in different areas based on their expertise and interests. Here are some common types of operations research analysts:

• Supply Chain Analyst: Supply chain analysts focus on optimizing supply chain operations, including demand forecasting, inventory management, distribution network design, transportation optimization, and supplier management. They work on improving efficiency, reducing costs, and enhancing overall supply chain performance.
• Production Planning Analyst: Production planning analysts specialize in optimizing production processes, capacity planning, scheduling, and resource allocation. They develop mathematical models and algorithms to determine the optimal production plan, considering factors such as machine capacity, labor availability, material constraints, and customer demand.
• Pricing Analyst: Pricing analysts focus on developing pricing strategies and models to maximize revenue and profitability. They use mathematical optimization and statistical analysis techniques to analyze market demand, competitor pricing, cost structures, and customer behavior, helping organizations set optimal prices for products and services.
• Financial Analyst : Financial analysts apply operations research techniques to financial planning, risk management, portfolio optimization, and investment decision-making. They develop models and algorithms to analyze financial data, evaluate investment options, and optimize financial performance while considering risk factors.
• Healthcare Analyst: Healthcare analysts apply operations research methods to optimize healthcare delivery systems, resource allocation, patient flow, and healthcare quality. They develop models and algorithms to improve hospital operations, appointment scheduling, staffing, and resource utilization in order to enhance patient outcomes and efficiency.
• Risk Analyst: Risk analysts specialize in assessing and managing risks in various industries. They develop mathematical models and simulation techniques to evaluate and mitigate risks associated with supply chain disruptions, financial investments, project management, and other operational areas.
• Decision Support Analyst: Decision support analysts assist organizations in making informed decisions by providing analytical insights and recommendations. They develop decision support systems, models, and visualization tools that help stakeholders understand complex data, evaluate options, and select the best course of action.
• Optimization Analyst: Optimization analysts focus on solving complex optimization problems using mathematical programming techniques. They develop and implement optimization models to address problems such as resource allocation, workforce scheduling, facility location, and network optimization.

Are you suited to be an operations research analyst?

Operations research analysts have distinct personalities . They tend to be investigative individuals, which means they’re intellectual, introspective, and inquisitive. They are curious, methodical, rational, analytical, and logical. Some of them are also conventional, meaning they’re conscientious and conservative.

Does this sound like you? Take our free career test to find out if operations research analyst is one of your top career matches.

What is the workplace of an Operations Research Analyst like?

Operations research analysts typically work in office settings, whether it's within a company or a consulting firm. They may also work remotely or engage in a combination of on-site and remote work, especially in situations where data and analysis can be accessed electronically. Their work involves extensive use of computers and specialized software tools for mathematical modeling, data analysis, and simulation.

Collaboration is an essential aspect of the work environment for operations research analysts. They often work closely with cross-functional teams, including managers, engineers, data scientists, and subject matter experts. This collaboration is important to gather relevant data, understand business processes, and gain insights into the problem or decision-making context. Operations research analysts may participate in meetings, workshops, or project teams to discuss findings, share progress, and align on goals.

The nature of their work also involves data-intensive tasks. Operations research analysts spend a significant amount of time collecting, cleaning, and analyzing data to inform their models and simulations. They use statistical software, programming languages, and database tools to process and manipulate large datasets. Additionally, they apply mathematical modeling techniques and optimization algorithms to derive insights, explore different scenarios, and identify optimal solutions.

In terms of work schedule, operations research analysts typically work full-time, following regular business hours. However, project deadlines or urgent issues may require flexibility and occasional overtime to meet deliverables. The workload can vary depending on the complexity and scope of the projects they are involved in.

Operations Research Analysts are also known as: OR Analyst Operations Analyst